BackgroundThe aim of this study was to quantitatively analyze quite standing postural stability of adolescent idiopathic scoliosis (AIS) patients in respect to three sensory systems (visual, vestibular, and somatosensory).MethodIn this study, we analyzed the anterior-posterior center of pressure (CoP) signal using discrete wavelet transform (DWT) between AIS patients (n = 32) and normal controls (n = 25) during quiet standing.ResultThe energy rate (∆EEYE%) of the CoP signal was significantly higher in the AIS group than that in the control group at levels corresponding to vestibular and somatosensory systems (p < 0.01).ConclusionsThis implies that AIS patients use strategies to compensate for possible head position changes and spinal asymmetry caused by morphological deformations of the spine through vestibular and somatosensory systems. This could be interpreted that such compensation could help them maintain postural stability during quiet standing. The interpretation of CoP signal during quiet standing in AIS patients will improve our understanding of changes in physical exercise ability due to morphological deformity of the spine. This result is useful for evaluating postural stability before and after treatments (spinal fusion, bracing, rehabilitation, and so on).

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Surface registration is an important factor in surgical navigation in determining the success rate and stability of surgery by providing the operator with the exact location of a lesion. The problem of surface registration is that point cloud in the patient space is acquired at irregular intervals due to the operator’s tracking speed and method. The purpose of this study is to analyze the effect of irregular intervals of point cloud caused by tracking speed and method on the registration accuracy. For this study, we created the head phantom to obtain a point cloud in the patient space with a similar object to that of a patient and acquired a point cloud in a total of ten times. In order to analyze the accuracy of registration according to the interval, cubic spline interpolation was applied to the existing point cloud. Additionally, irregular intervals of the point cloud were regenerated into regular intervals. As a result of applying the regenerated point cloud to the surface registration, the surface registration error was not statistically different from the existing point cloud. However, the target registration error significantly lower (p < 0.01). These results indicate that the intervals of point cloud affect the accuracy of registration, and that point cloud with regular intervals can improve the surface registration accuracy.

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The aim of this research was to quantify the coordination pattern between thorax and pelvis during a golf swing. The coordination patterns were calculated using vector coding technique, which had been applied to quantify the coordination changes in coupling angle (γ) between two different segments. For this, fifteen professional and fifteen amateur golfers who had no significant history of musculoskeletal injuries. There was no significant difference in coordination patterns between the two groups for rotation motion during backswing (p = 0.333). On the other hand, during the downswing phase, there were significant differences between professional and amateur groups in all motions (flexion/extension: professional [γ] = 187.8°, amateur [γ] = 167.4°; side bending: professional [γ] = 288.4°, amateur [γ] = 245.7°; rotation: professional [γ] = 232.0°, amateur [γ] = 229.5°). These results are expected to be a discriminating measure to assess complex coordination of golfers' trunk movements and preliminary study for interesting comparison by golf skilled levels.

Background: Although idiopathic Parkinson’s disease (IPD) is increasing with the aging population, there is no adequate screening test for early diagnosis of IPD. Cardiac autonomic dysfunction begins in the early stages of IPD, and an electrocardiogram (ECG) contains precise information on the heart. Objective: This study is to develop an ECG deep learning algorithm that can efficiently screen for IPD. Methods: Data were collected from 751 IPD patients (2,138 ECGs), 751 age and sex-matched non-IPD patients (2,673 ECGs) as a control group, and 297 drug-induced Parkinsonism (DPD) patients (875 ECGs) as a disease control group. ECG data were randomly divided into training set, validation set, and test set at a ratio of 6:2:2. We developed a deep-convolutional neural network (CNN) consisting of 16 layers with Bayesian optimization that classified IPD patients by ECG data. The robustness of the deep learning model was verified through 5-fold cross-validation. Results: The AUROC of the model for detection of IPD was 0.924 (95% CI, 0.913–0.936) in the test set. That for detecting DPD was 0.473 (95% CI, 0.453–0.504). The sensitivities of the model according to Unified Parkinson’s Disease Rating Scale III and Hoehn & Yahr scale were also similar. Conclusion: In conclusion, the CNN-based deep learning model using ECG data showed quite good performance in identifying IPD patients. Standardized 12-lead ECG test could be one of the clinically feasible candidate methods for early screening of IPD in the future.

Background Although the surface registration technique has the advantage of being relatively safe and the operation time is short, it generally has the disadvantage of low accuracy. Purpose This research proposes automated machine learning (AutoML)‐based surface registration to improve the accuracy of image‐guided surgical navigation systems. Methods The state‐of‐the‐art surface registration concept is that first, using a neural network model, a new point‐cloud that matches the facial information acquired by a passive probe of an optical tracking system (OTS) is extracted from the facial information obtained by computerized tomography. Target registration error (TRE) representing the accuracy of surface registration is then calculated by applying the iterative closest point (ICP) algorithm to the newly extracted point‐cloud and OTS information. In this process, the hyperparameters used in the neural network model and ICP algorithm are automatically optimized using Bayesian optimization with expected improvement to yield improved registration accuracy. Results Using the proposed surface registration methodology, the average TRE for the targets located in the sinus space and nasal cavity of the soft phantoms is 0.939 ± 0.375 mm, which shows 57.8% improvement compared to the average TRE of 2.227 ± 0.193 mm calculated by the conventional surface registration method (p < 0.01). The performance of the proposed methodology is evaluated, and the average TREs computed by the proposed methodology and the conventional method are 0.767 ± 0.132 and 2.615 ± 0.378 mm, respectively. Additionally, for one healthy adult, the clinical applicability of the AutoML‐based surface registration is also presented. Conclusion Our findings showed that the registration accuracy could be improved while maintaining the advantages of the surface registration technique.

Background Improved prediction of atrial fibrillation (AF) may allow for earlier interventions for stroke prevention, as well as mortality and morbidity from other AF‐related complications. We developed a clinically feasible and accurate AF prediction model using electronic health records and computerized ECG interpretation. Methods and Results A total of 671 318 patients were screened from 3 tertiary hospitals. After careful exclusion of cases with missing values and a prior AF diagnosis, AF prediction models were developed from the derivation cohort of 25 584 patients without AF at baseline. In the internal/external validation cohort of 117 523 patients, the model using 6 clinical features and 5 ECG diagnoses showed the highest performance for 3‐year new‐onset AF prediction (C‐statistic, 0.796 [95% CI, 0.785–0.806]). A more simplified model using age, sex, and 5 ECG diagnoses (atrioventricular block, fusion beats, marked sinus arrhythmia, supraventricular premature complex, and wide QRS complex) had comparable predictive power (C‐statistic, 0.777 [95% CI, 0.766–0.788]). The simplified model showed a similar or better predictive performance than the previous models. In the subgroup analysis, the models performed relatively better in patients without risk factors. Specifically, the predictive power was lower in patients with heart failure or decreased renal function. Conclusions Although the 3‐year AF prediction model using both clinical and ECG variables showed the highest performance, the simplified model using age, sex, and 5 ECG diagnoses also had a comparable prediction power with broad applicability for incident AF.